CN116450801A

CN116450801A - Program learning method, apparatus, device and storage medium

Info

Publication number: CN116450801A
Application number: CN202310317519.1A
Authority: CN
Inventors: 王宇航; 尤振宇; 孙洪伟; 邓周
Original assignee: Beijing Siming Qichuang Technology Co ltd
Current assignee: Beijing Siming Qichuang Technology Co ltd
Priority date: 2023-03-29
Filing date: 2023-03-29
Publication date: 2023-07-18

Abstract

The invention discloses a programming learning method, a device, equipment and a storage medium, which relate to the technical field of programming education, wherein the programming method comprises the following steps: acquiring learning behavior data of a learner on recommended content in a programming mode; wherein the recommended content is determined from a programming knowledge graph; and determining the current mastering condition of the learner on programming knowledge according to the learning behavior data. Through the technical scheme, accurate and personalized content recommendation is realized according to the behavior data generated in the learning process of the learner, personalized content service aiming at individual characteristics of the learner is provided on a large scale, so that the learner can truly apply and test the learned knowledge, and a higher learning target is achieved.

Description

Program learning method, apparatus, device and storage medium

Technical Field

The present invention relates to the field of programming education, and in particular, to a program learning method, apparatus, device, and storage medium.

Background

With the continuous promotion of diathesis education and the improvement of living standard, more and more people start to learn and program, and the learning and programming not only has help on the aspects of mathematics and scientific knowledge, but also has positive and important roles on the aspects of language, creativity, social communication and the like.

In the prior art, the traditional programming needs to learn a large amount of instructions, function names and other contents through common video watching and question brushing, is a complex and difficult process, is not suitable for being quickly put on hand, is easy to lose the interest of further learning programming, and is difficult to achieve a higher learning target only by video learning and common exercise.

Disclosure of Invention

The invention provides a programming learning method, a programming learning device, programming learning equipment and a storage medium, so as to realize personalized guidance of programming learning.

In a first aspect, a program learning method is provided, the method comprising:

acquiring learning behavior data of a learner on recommended content in a programming mode; wherein the recommended content is determined from a programming knowledge graph;

and determining the current mastering condition of the learner on programming knowledge according to the learning behavior data.

In a second aspect, there is provided a program learning apparatus, the apparatus comprising:

the learning behavior data acquisition module is used for acquiring learning behavior data of recommended content by a learner in a programming mode; wherein the recommended content is determined from a programming knowledge graph;

and the mastering condition determining module is used for determining the current mastering condition of the learner on the programming knowledge according to the learning behavior data.

In a third aspect, an embodiment of the present invention further provides a program learning system, including:

the programming learning platform is used for acquiring learning behavior data of a learner on recommended content in a programming mode; wherein the recommended content is determined from a programming knowledge graph; determining the current mastering condition of the learner on programming knowledge according to the learning behavior data;

and the learner client is used for learning the recommended content by the learner in the programming mode and generating learning behavior data.

In a fourth aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the program learning method according to any one of the embodiments of the present invention when executing the program.

In a fifth aspect, embodiments of the present invention further provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a program learning method according to any of the embodiments of the present invention.

According to the technical scheme, the recommended content is determined from the programming knowledge graph, learning behavior data of the learner on the recommended content in any programming mode is obtained, and the current mastering condition of the learner on programming knowledge in the recommended content is determined according to the obtained learning behavior data. According to the technical scheme, accurate and personalized content recommendation is realized according to the behavior data generated in the learning process of the learner, the teaching quality is improved, and meanwhile, personalized content service aiming at individual characteristics of the learner can be provided in a large scale.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a program learning method according to a first embodiment of the present invention;

FIG. 2 is a flow chart of a program learning method according to a second embodiment of the present invention;

FIG. 3 is a flow chart of a program learning method according to a third embodiment of the present invention;

FIG. 4 is a flow chart of a program learning method according to a fourth embodiment of the present invention;

fig. 5 is a schematic structural diagram of a programming learning device according to a fifth embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device implementing a program learning method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "current," "history," "first," "second," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In addition, it should be noted that, in the technical scheme of the invention, the processes of collection, storage, use, processing, transmission, provision, disclosure and the like of the related learning behavior data and the like all conform to the regulations of related laws and regulations and do not violate the popular regulations.

Example 1

Fig. 1 is a flowchart of a program learning method provided in an embodiment of the present invention, where the method may be applied to a situation how to perform personalized instruction teaching on a learner in a program teaching scenario, and the method may be performed by a program learning device, where the program learning device may be implemented in hardware and/or software, and the program learning device may be integrated into an electronic device that carries a program learning function, for example, a programming platform in a client device or a server device, where the client device may be a tablet computer, a desktop computer, or the like. As shown in fig. 1, the method includes:

s110, learning behavior data of the learner on the recommended content in the programming mode is obtained.

Wherein the recommended content is determined from a programming knowledge graph.

The learning party refers to a party who needs to perform programming learning, and may be, for example, a student.

The programming mode refers to programming learning modes with different difficulty levels and different forms; the difficulty of different programming models is different; the content learned by different learners in the same programming mode has different difficulties. Optionally, the programming modes may include a pre-class preparation mode, an interlope type course mode, an error question book mode, a stage evaluation mode, an extended class mode, a free creation mode, a exercise question adding mode, an event mode, an online fight mode, and the like. It should be noted that, the difficulty level of the pre-class preparation mode, the related course mode, the wrong topic book mode, the stage evaluation mode, the extended classroom mode, the free creation mode, the exercise topic mode, the event mode and the online fight mode is sequentially increased, the programming mode of each difficulty has a teaching target corresponding to the exercise topic mode, the learner gradually improves the own learning target corresponding to the teaching target by programming learning under the teaching target of the programming mode with increased difficulty, and optionally, the learning target can include from high to low: memory, understanding, application, analysis, evaluation, and creation.

Illustratively, the teaching objectives of the pre-class preparation mode, the interloper class mode, and the mistopic mode are memory and understanding. The pre-class preparation mode is classroom evaluation before the starting of the joint class mode, generally the first class is an open question, some cognition of programming is examined, knowledge points are not involved, and from the second class, the programming platform can automatically generate test questions corresponding to the class knowledge points according to the evaluation condition of the learner on the class knowledge points, mainly review the class knowledge points, and check the memory and understanding of the learner on the main knowledge points. The method comprises the steps that course learning is realized in an interlope mode, a first preset number of main knowledge points are usually set for teaching of each course, and scenario introduction exists in the process; the learner participates in the scenario at a first view angle to help the personas in the scenario to complete tasks, and after the scenario is introduced, the learner can explain the first knowledge point, and the learner can complete the learning of the knowledge point by using a small closed loop comprising video explanation, break-through and test questions; one knowledge point is usually provided with a second preset number of small closed loops, the interaction form of each closed loop is different, the break-over difficulty is slightly improved, the learning party can obtain achievement in time by matching with timely feedback, and the understanding and grasping degree of the learning party to the knowledge point are checked through the mode of fighting on a programming platform. The wrong question book mode comprises all questions which are first answered by a learner in a class of pre-class preparation and break-through class, the learner needs to practice the wrong questions again and at least needs to answer the questions with the same knowledge point for more than the first preset times, and besides the wrong questions are rehearsal, the programming platform can also generate different types of questions according to the knowledge points of the wrong questions. For example, when the learner's choice questions of the knowledge point are examined by how many steps to move, the programming platform can adjust the variable value according to the knowledge point to generate a new code, generate choice questions or fill in space for the learner to answer, and for each wrong question, the programming platform can generate at least a third preset number of test questions corresponding to the number of questions so as to examine whether the learner really understands and grasps. Specifically, the programming platform may generate the test questions based on the following manner:

The programming platform reads the question configuration corresponding to the knowledge points and generates a question variable value of the new test question according to the variable range in the question configuration;

randomly selecting a section of keyword groups describing configuration according to description through a description set associated with knowledge points in a database, and generating a question description of a new test question;

and generating a question answer of the current new test question according to a preset calculation formula in the configuration.

If the new test question is a gap-filling question, selecting a question answer corresponding to the new test question according to a key phrase in the question configuration; if the new test question is a selection question, generating a choice (i.e. a question answer) corresponding to the new test question according to the answer set configured by the new question description.

If the number of errors of the measurement of a learning party at one knowledge point continuously exceeds the second preset number, the programming platform pops up the knowledge point explanation video to enable the learning party to learn again, so that the learning party can learn better and understand the learning content of the next step, and the situation that the learning party learns the next step to generate the anaerobic mind under the condition that the number of the knowledge points which are not mastered is increased is avoided. The first preset number, the second preset number, the third preset number, the first preset number of times and the second preset number of times can be set by a person skilled in the art according to actual situations.

Illustratively, the teaching objectives of the stage assessment mode are memory, understanding and application. The stage evaluation mode sets a knowledge point for main evaluation for each chapter, and the stage evaluation test evaluates the mastering condition of the learner on the main knowledge point of the chapter, which is generally set in the last lesson of each chapter, so that the evaluation content is not only the application of a single knowledge point, but the comprehensive application of the knowledge point learned by the whole chapter and the knowledge point learned by the previous chapter. The stage evaluation mode mainly comprises the steps of answering basic test questions and making a gate break, wherein the theme of the basic test questions is biased to be gamified. The programming platform automatically generates test questions according to the learned knowledge points in the range of the learned knowledge points, sets answer time limit, for example, completes fixed number of test questions in a specified preset time length, sets countdown for each test question, on one hand, tests the mastering condition of a learner on the knowledge points, on the other hand, tests the strain capacity and logic thinking capacity of a learner in a short time, makes the test process more interesting and challenging, enters into a break-over link after the examination and clearance of basic test questions, is comprehensive application of the Level learned knowledge points, and the break-over link usually has a theme, for example, controls small walnut to move and avoid cannonballs to rescue the cannonball launched by the cannon platform, and requires the learner to use the main knowledge points learned in the section to complete break-over in cooperation with other learned knowledge points. The preset time period and the fixed number can be set by a person skilled in the art according to actual situations.

Illustratively, teaching objectives of expanding class patterns are application and analysis. The development classroom generates semi-open theme training or checkpoint clearance training matched with learning behavior data of a learner according to the learning behavior data of the learner; the learner may need to complete one or more training programs as desired. When a learner encounters difficulty in the process of a project or a challenge, for example, the learner has too long duration in one checkpoint or the project, the programming platform triggers a prompt, the learner can find an explanation example or a code application example of a knowledge point required by the checkpoint in the prompt, and the difficulty of the clearance of the project or the checkpoint can be reduced according to the prompt selection. The embodiment can realize real-time pre-monitoring of each learner and provide personalized content recommendation for each learner.

Illustratively, the teaching goal of the free authoring mode is application, analysis, and evaluation. Compared with the expansion classroom mode, the free creation mode has higher degree of freedom and higher opening degree. The free creation mode does not limit the learned knowledge points in the course, and functionally, the programming platform presets a half creation project except for the function of free creation provided for a learner, wherein the preset half creation project comprises preset codes, and the learner can continue to create on the basis of the preset codes; meanwhile, the programming platform is also provided with sharing and preview functions, learning parties can preview and share creation items mutually, and interested item codes can be downloaded to continue creation. Specifically, the method can be realized by setting the permission mode, for example, the method can be that after the learner creates the permission, the learner shares the permission to other learners in the form of a shared link, when the learner who receives the address (link) accesses the link, the programming platform detects the sharing validity of the link, and if the permission of the current work is added to the shared party after the detection is passed, the permission includes but is not limited to a viewing permission, an operating permission and an editing permission.

Furthermore, in the free creation mode, the learner can write codes and combine the codes with hardware, and the written codes are downloaded to the hardware, such as a ticker, a car, a robot and the like, and the learner can program the codes by combining the hardware, so that the codes are really applied to practice. Specifically, the programming platform is provided with a code conversion module, and the module enables the original code to be adapted to hardware operation by adding a preset code and modifying the name of the corresponding function; after the code is successfully converted, the code is downloaded to hardware for operation.

In addition, in the free creation mode, the learner can upload the project codes written by the learner to the display area, and the learner can evaluate each other and perform top-mounted display on some excellent projects. It should be noted that the excellent project can be directly operated on the web side of different learners, so that the full-end operation is realized.

Illustratively, the teaching goal of the exercise question model is memory and understanding. The exercise mode surveys the comprehensive application of a plurality of knowledge points, for example, the error codes of students in the programming exercise process are acquired, a plurality of knowledge points are involved, and exercise questions are pushed to corresponding learners according to the range of the involved knowledge points. Besides writing codes, the actual programming project is a code debugging process, and the problem of which link of the codes in the running process and the efficiency of thinking of the code running are judged by running the code checking effect so as to strengthen the actual combat awareness of students.

Illustratively, the teaching goal of the event pattern is application and analysis; the teaching targets of the online combat mode are application, analysis, evaluation and creation.

The programming mode in the embodiment can provide omnibearing assessment and exercise for a learner, so that the learner memorizes and understands the programming knowledge, repeatedly uses and checks the learned programming knowledge, flexibly uses the learned programming knowledge, and gradually achieves the final learning goal.

The programming knowledge graph refers to a graph containing all programming learning contents. Optionally, the programming knowledge graph includes at least one programming knowledge category; the programming knowledge category includes at least one programming knowledge point; the knowledge points correspond to at least one content resource; the content resources include at least one of: the knowledge points explain videos, test questions and practice checkpoints; the programming knowledge points are the minimum granularity of inseparable in the programming knowledge graph; the sides with arrows between different programming knowledge points represent the learning order of the programming knowledge points.

The learning behavior data is behavior data for performing programming learning in the programming mode. Optionally, the learning behavior data includes at least one of: first learning behavior data of a knowledge point explanation video, second learning behavior data of an exercise checkpoint, third learning behavior data of a test question and program writing data; the first learning behavior data refer to behavior data related to a learner when watching a knowledge point explanation video; the second learning behavior data refers to behavior data related to the programming learning of a learner through an exercise checkpoint; the third learning behavior data refers to behavior data that the learner involves in solving the programming topic. Wherein, the first learning behavior data may include a viewing duration and/or a viewing number; the second learning behavior data may include at least one of: stay time, try times, whether to look over auxiliary prompt and whether to look back knowledge point explanation video; the third learning behavior data may include at least one of: the time length of the first answer, the accuracy of the first answer and the accuracy of the second answer.

The recommended content refers to learning content presented to a learner in a programming mode; alternatively, the recommended content may be a programmed knowledge point of the learner that is recommended according to the capability level of the learner, and may be determined from the programmed knowledge graph.

Alternatively, the recommended content is determined from a programming knowledge graph, comprising: acquiring historical learning behavior data of a learner in a programming mode; determining learning behaviors and capability images of a learner according to the historical learning behavior data; and determining recommended content from the programming knowledge graph according to learning behaviors and capability portraits of the learner, and pushing the recommended content to the learner.

The history learning behavior data refers to behavior data of program learning in a program mode under history.

Specifically, historical learning behavior data of the learner in the programming mode can be obtained, then, based on the capability portrait determining model, the learning behavior and the capability portrait of the learner can be determined according to the historical learning behavior data, and further, corresponding recommended content is determined for the learner from the programming knowledge graph according to the learning behavior and the capability portrait of the learner, and the recommended content is pushed to the learner. Wherein the capability representation determination model may be derived based on a machine learning algorithm.

In addition, in the initial stage of each programming mode, the programming platform can be used for downloading the lessons (i.e. the most basic learning content) to the local of the learner by different learners, that is, a set of most basic learning content which is suitable for all learners exists in the programming platform, so that the problem that the learner cannot obtain the recommended content due to poor network or other abnormal situations can be solved.

Specifically, in response to a learning operation of a learner in any programming mode in the programming platform, learning behavior data of the learner on recommended content in the programming mode is obtained. The learning operation may be clicking, fast forwarding, dragging, inputting operation, etc.

S120, determining the current mastering condition of the learner on the programming knowledge according to the learning behavior data.

The current mastering situation refers to the learning mastering situation of the learner on programming knowledge in the current programming mode; alternatively, it may be expressed in the form of a score, a grade, or the like.

Specifically, the current mastering situation of the programming knowledge by the learner can be determined based on the mastering situation determination model according to the learning behavior data. Wherein the mastery situation determination model can be derived based on a deep learning algorithm.

According to the embodiment of the invention, the recommended content is determined from the programming knowledge graph, the learning behavior data of the learner on the recommended content in any programming mode is obtained, the current mastering condition of the learner on the programming knowledge existing in the recommended content is determined according to the obtained learning behavior data, so that the accurate and personalized content recommendation according to the behavior data generated in the learning process of the learner is realized, the teaching quality is improved, meanwhile, personalized content service aiming at the individual characteristics of the learner can be provided on a large scale, the learner can truly apply and test the learned knowledge, and a higher learning target is achieved.

Example two

Fig. 2 is a flowchart of a program learning method according to a second embodiment of the present invention, where the present embodiment is further refined based on the foregoing embodiment, and specific steps for determining, according to learning behavior data, a current grasping condition of programming knowledge by a learner are provided. As shown in fig. 2, the method includes:

s210, learning behavior data of a learner on recommended content in a programming mode is obtained.

S220, determining dimension scores of the learner in preset dimensions according to the learning behavior data.

The preset dimension refers to the dimension for judging programming learning quality in a programming mode; optionally, the preset dimension may include at least one of the following dimensions: rigor, logic, concentration, knowledge, and creativity. Dimension scoring refers to scoring of a programming learning process in a preset dimension in a programming mode; it should be noted that the corresponding dimension scoring rules of different preset dimensions are different.

Alternatively, a dimension score of the learner in the preset dimension may be determined according to learning behavior data based on a preset dimension score calculation rule. For example, the dimension score for the rigor dimension may be determined based on the percentage of rank in the current learner as a function of the hall score and the average number of clearance times, e.g., may be determined by the following equation:

the dimension score of the stringency dimension = 70+ [ 0.6+ as measured by hall score (1-rank percentage of the mean of the number of clearance times in the study party at this stage) ×40] ×0.3.

For another example, the dimension score of the logic force dimension may be determined according to the rank percentage of the online time length of the learner in the current learning party, the continuous maximum clearance number of the learner and the primary clearance ratio of the learner, for example, may be determined by the following formula:

Dimension score of logic force dimension = 70+ [ (1-rank percentage of the learning party online time length in the learning party of the present period) ×80+learning party continuous maximum number of passes ratio ×10+learning Fang Yici number of passes ratio ×10] ×0.3.

For another example, the dimension score of the concentration dimension may be determined according to the online time length and the total lesson completion time length of the learner, for example, may be determined by the following formula:

concentration dimension score = 70+ (learner online duration/learner total duration of lesson).

For another example, the dimension score of the knowledge dimension may be a percentage weight set according to the first answer accuracy of each knowledge point by the learner, where the percentage weight represents the mastery of the knowledge point by the learner, and the percentage weight of the first answer accuracy of the wrong knowledge point is updated according to the consolidation of the wrong knowledge point by the learner.

For another example, the dimension score of the creativity dimension may be determined comprehensively from the number of items produced in the free creation mode and the online combat mode, and the score of the lesson guide and the executive owner.

Wherein the dimension score for the creativity dimension may be 1-10 minutes.

In yet another alternative, a dimension score of the learner in the preset dimension may be determined based on the five-force model according to the learning behavior data. Specifically, learning behavior data corresponding to a preset dimension can be input into a five-force model, and dimension scores of a learner in the preset dimension are obtained through model processing.

The five-force model can be obtained through training according to historical learning behavior data based on a machine learning algorithm. Specifically, training data required by model learning is prepared first, historical learning behavior data of a learner is divided into 5 grades (A, B +, B, B-, C), historical learning behavior data corresponding to the grades A, B + and B are selected as positive samples, and historical learning behavior data corresponding to the grades B and C are selected as negative samples.

Further, the five-force model comprises two parts, wherein one part is a grading card model, and the other part is a fraction conversion model; the scoring card model may adopt a logistic model, specifically the following formula:

wherein, the liquid crystal display device comprises a liquid crystal display device,representing the winning rate, p representing the positive sample probability; omega ₁ ,ω ₂ ,…,ω _i ,…,ω _n Respectively representing parameters required by the model; x is x ₁ ,x ₂ ,…,i,…,x _n Respectively representing the input training data.

Further, the fractional conversion model may be implemented by the following formula:

score＝A-B*ln(odds)

where score represents the final output dimension score; A. b are parameters in the fractional conversion model respectively. Wherein the determination of parameters a and B involves the following two preconditions: 1) A reference score in a certain preset dimension, denoted P0; 2) When odds doubles, score decreases, which is noted as PDO (point of double odds).

Based on the two preconditions, the following two formulas can be obtained:

P0＝A-B*ln(odds)

PDO＝A-B*ln(2*odds)

based on the two formulas, the method can be solved to obtainA＝P0+B*ln(2*odds)。

In conclusion, a five-force model can be obtained. It should be noted that after score is obtained, score can be linearly mapped to 0-100 points according to the distribution of the learner.

Specifically, different dimensionalities of the learner in the learning process are scored according to learning behavior data of a certain lesson of the learner in any mode, so as to obtain comprehensive evaluation of the learner in the learning process. The learning system can calculate the five-force score of the learner at each class according to the learning behavior data of the learner at each class in the joint class mode based on the five-force model, and the five-force score can be called by a learning report when the learner triggers the course of the class to finish class, and the programming platform can give an evaluation to the comprehensive performance of the learner at the class.

S230, determining the current mastering condition of the learner on the programming knowledge according to the dimension scores.

Specifically, the current mastering situation of the learner for the programming knowledge points is determined according to the scores of different dimensions. For example, the dimension score of the concentration dimension can be used for finding the learning stay time of the learner at the knowledge point, and the dimension score of the logic force dimension can represent the ability of the learner to digest and understand the knowledge point; the learning ability, learning efficiency and acceptance ability of the learning party are different, and the five-force score calculated by the five-force model can show the mastering condition of the learning party on the knowledge point to a certain degree.

Optionally, after determining the current mastering situation of the programming knowledge by the learner according to the dimension score, the recommended content may be determined again from the programming knowledge graph according to the dimension score.

For example, the logic force score may represent the ability of a learner to digest and understand knowledge points, and for a learner with a high logic force ranking, a learner with a higher difficulty in recommending learning knowledge points in subsequent courses of learning.

According to the learning method, the learning party is determined to score in the preset dimension through the learning behavior data, the content recommendation suitable for the learning party is pushed to the learning party according to the score in the preset dimension, the accurate and personalized content recommendation is realized according to the behavior data generated in the learning process of the learning party, the teaching quality is improved, meanwhile, personalized content service aiming at individual characteristics of the learning party can be provided on a large scale, the learning party can truly obtain application and inspection of the learned knowledge, and a higher learning target is achieved.

Example III

Fig. 3 is a flowchart of a program learning method according to a third embodiment of the present invention, where the program learning method is further refined based on the foregoing embodiment, and specific steps for acquiring learning behavior data of a learner on recommended content in the program mode are provided if the program mode is an event mode. As shown in fig. 3, the method includes:

S310, determining the event level corresponding to the learner.

The event level refers to the difficulty level of the event. Alternatively, the level of the event may be determined according to the range of knowledge points mastered by the learner and the history mastering conditions, for example, a certain level of event may check the knowledge points ABCDE, while the knowledge points learned by the learner a include ABCDEF, including the knowledge points to be checked by the event, and the assessment learner a may participate in the level of event.

S320, learning behavior data of the learner at the corresponding event level in the event mode is obtained.

Wherein the event mode belongs to one of the programming modes; optionally, the event question library in the event mode comprises various real questions of the event in the past year counted according to the knowledge point labels, and the programming platform can reasonably predict the distribution condition of knowledge points of future event according to the distribution condition of knowledge points of the historical real questions of the event with the knowledge point labels and automatically generate the simulation test questions.

Alternatively, only those learners who are able to participate in the event mode reach a preset programming level through a programming learning process. Alternatively, the preset programming level may be set by the programming platform rationally.

Specifically, program writing data (i.e., program codes) of corresponding event levels in the event mode submitted by the learner are obtained.

S330, based on the preset code index, determining the current mastering condition of the learner on the programming knowledge according to the learning behavior data.

The preset code index may refer to readability, conciseness, expandability and reusability of the code, and is used for evaluating the quality level of the programming code.

Specifically, according to the program codes submitted by the learner at the event level corresponding to the event mode, scoring and suggesting the submitted program codes from the readability, conciseness, expandability and reusability of the codes.

Optionally, after scoring the submitted program code and making the suggestion, the method further includes:

the learner can process and submit the program codes submitted last time according to the proposed proposal, and score and evaluate again after the program codes are submitted, so that a circulating closed loop is formed, and the learner is promoted to improve the quality of the codes.

In the embodiment of the invention, the learner participating in the event mode has a certain level of programming capability, and the learner continuously improves the quality of codes on the basis of ensuring the operability of the program codes through the processes of submitting, processing and re-submitting, so that the codes written by the learner accord with the preset code indexes.

Example IV

Fig. 4 is a flowchart of a program learning method according to a fourth embodiment of the present invention, where the embodiment is further refined based on the foregoing embodiment, and specific steps of acquiring learning behavior data of a learner on recommended content in a program mode are provided if the program mode is an online fight mode. As shown in fig. 4, the method includes:

s410, determining a target combat sub-mode in response to the selection operation of the learner on the sub-combat mode in the online combat mode.

Wherein the online fight mode belongs to one of the programming modes. Optionally, an online combat mode is presented in a gambling form, in which real-time collaboration and/or combat between the learner and the learner may be achieved.

The sub-combat mode can comprise a single-person mode and a multi-person mode, and the number of game characters in the multi-person mode is the number of participating learners.

Optionally, the learner may select a single person mode or a multi-person mode in the online combat mode; accordingly, the programming platform may determine the single person mode or the multiple person mode selected by the learner as the target combat mode. For example, a corresponding game may be recommended to the learner through learning behavior data of the learner, wherein the game may be in a multiplayer mode; further, a learner may be provided with a teammate or opponent of comparable strength.

S420, determining the target game role in response to the selection operation of the target combat sub-mode game role by the learner.

The game characters are characters which need to be controlled to complete the fight in the online fight mode, and the game characters selected by the learner are target game characters corresponding to the learner.

The selection operation may refer to a click operation or the like of the learner in the programming platform.

Specifically, the learner clicks the game role desired by the learner in the online fight model in the programming platform; correspondingly, the programming platform takes the game role as the target game role of the learner. For example, if in the multi-person mode, the programming platform may also match each learner and assign corresponding game characters to different learners in the same scene. For another example, in the cooperative mode, different tasks may be allocated to different target game characters (learning parties) according to a task list of the game, or allocation of game tasks may be implemented in response to a selection operation of the tasks by the different learning parties (target game characters). Further, the code (behavior control code) written by each learner controls the corresponding character to complete the corresponding task.

S430, acquiring behavior control codes written by the learner for the target game roles.

The behavior control code refers to logic program code of a control target game role conforming to a game rule, and is generated by a learner; it should be noted that, all actions generated by the target game character must be based on the logic program code, for example, if it is desired to control the movement of the target game character, first, a rule code for controlling the movement of the target game character must be present in the logic program code, for example, when an up-down-left-right command entered by a keyboard is received, the control code for the movement of the target game character is performed up-down-left-right.

Specifically, the learner can write a behavior control code for the target game character according to a specific project task, click an operation button in the interface, and upload the behavior control code to the programming platform; correspondingly, after the programming platform acquires the target game role behavior control codes under the corresponding scene, the programming platform starts to run the codes.

Further, a server side of the programming platform runs a behavior control code of a target game character and collects a game running log, wherein the game running log is transmitted to a learner in a long connection mode; the client of the learning party receives the log data transmitted through the long connection, acquires the role instruction according to the log data, and operates the game role to play. It should be noted that, the video data taken by the learner is relatively large, and in this embodiment, the programming platform returns only the data of the key frames to the learner.

Furthermore, a set of key frame instructions (instructions of game roles, namely key instructions) can be predefined, and after the key frame instructions run in the system, the key frame instructions are output to a client of a learner in a log form; correspondingly, the client receives a key frame instruction, and performs playing for realizing the game operation effect according to the key frame instruction, wherein the key frame instruction is a key frame instruction negotiated by the client and the server.

Optionally, the learner may write the behavior control code corresponding to the target game character according to the mastered programming knowledge (not limited to the programming knowledge involved in the course) according to the own thinking logic, so as to increase the degree of freedom of the online fight mode, specifically, the code written by the learner may obtain the environmental data provided in the game scene by receiving the information such as the position, speed, angle, etc. of the bullet in the game scene; upon receipt of the entry data, the learner designs in the environment execution strategies for the game character, such as how to accelerate, how to turn left, how to shoot, etc., the goals of which are to win the game winner with high efficiency. It should be noted that, the platform judges the operation result in an event-driven manner, and does not limit the execution strategy of the learner, thereby realizing the diversification of the design scheme of the learner. The degree of autonomy of online fight is relatively high. The creativity of the learner can be brought into full play, and the behavior control codes are uploaded to the programming platform; or writing corresponding behavior control code in the programming platform. Correspondingly, the programming platform can acquire behavior control codes written by the learner for the target game characters.

S440, determining target execution actions of the target game roles according to the behavior control codes so as to control the target game roles to execute the target execution actions until the game is finished, and recording the game process.

The target execution action refers to an execution action, such as execution, left turn, right turn, etc., which is made by the target game character according to the behavior control code and corresponds to the behavior control code.

Specifically, the programming platform may generate, based on a preset code action conversion rule, a behavior control code written by the learner, so as to control the target execution action of the target game character game, and further control the target game character to execute the corresponding target execution action until the game is ended. It should be noted that, the game items in the programming platform all have instructions describing the game target, the game character, the definition of the in-out parameter of the game character, and the corresponding out parameter; the learner can learn the input meaning and output corresponding instruction of the game character, for example, outputting left turn to indicate left turn of the control character, outputting grabbing to indicate grabbing of the precious stone by the control character, etc. After the learner submits the codes, the commands output by running are converted into corresponding action commands by the programming platform and transmitted to the game roles, for example, the learner outputs left turn, and the programming platform converts the commands into left turn of the game outputs, namely, the learner codes are used for controlling the actions of the game roles.

Meanwhile, the programming platform can record the whole game process, so that the subsequent repeated disc of the learner is facilitated, namely, an execution instruction is sent to the learner, and the server (programming platform) can be stored in real time and used for subsequent sharing or repeated disc.

In addition, it should be noted that the programming platform provides a video function, so that the learner can communicate with other learners in real time in a video manner to generate a behavior control code to complete the game challenge in a multi-player mode. Namely, the multi-person mode is a cooperation mode, and the discussion of targets can be carried out among different learners; when the programming platform distributes tasks, the tasks are split into a plurality of blocks. On one hand, the learner can communicate with a specific distribution mode of the task or discuss specific realization logic and the like through the video function; in addition, the codes can be synchronously displayed to the cooperators in the game editing process in the form of instructions, and the cooperators can see the real-time change of the codes. It should be noted that in this embodiment, the cooperator can only see the code editing process, but cannot directly write the other code, so as to ensure that the codes of the learner are self-edited and avoid cheating. It is noted that the video functions are independent functions in the programming platform.

S450, determining the current mastering condition of the learner on programming knowledge according to the game process and the behavior control code.

Alternatively, the current mastering situation of the learner on the programming knowledge can be determined according to the completion situation of the task in the game process and the quality of the behavior control code. For example, it is possible to determine a task execution score according to the completion condition of a task in the course of a game, and determine a quality score of a behavior control code based on a code evaluation index, and then take the result of adding the task execution score and the quality score as the current grasp condition of a learner on programming knowledge.

In some examples, if the online combat mode issues a task of grabbing precious stones, then who can use the least amount of code, the faster the operation, and who will win. If the online fight mode simulates a team cooperation to jointly complete a game item, such as a gold miner game, whether the team cooperation can jointly produce a game which is played or not is checked, and all codes are combined into one item to be smoothly operated. In addition to the code writing quality, the part considers the quantization index of the code quality, and whether the whole meets four indexes of readability, conciseness, expandability and reusability or not truly realizes creation through the learned knowledge. Specifically, for readability, judging whether the names defined by the learner are popular or easy to understand by enumerating the function names and variable names defined by the user and comparing the function names with the function module names; for simplicity, the scoring interval is designed by the number of lines of code, e.g., 5 lines may end, with a code score of more than 5 lines being low. For reusability, the number of classes defined by a learner is identified by cutting submitted codes, and the number of abstract function functions is used for judging reusability. For expandability, the number of identical codes in the learner code is detected, and if the number of identical codes is higher, the reusability is poor.

In another alternative way, the completion score of the game task in the game can be used as the input of the five-force model, and the final knowledge point mastering score, namely the current mastering condition of the learner on the programming knowledge in the behavior control code, can be output through the five-force model processing.

According to the embodiment of the invention, through introducing a gambling programming process and experience, the problem that the learning party is not interesting in the learning process is solved in experience, and online fight and cooperation can be carried out with a plurality of learning parties, so that the learning process has interaction and atmosphere sense, and the behavior control codes of the learning parties are uniformly operated in a server without downloading redundant clients.

On the basis of the above embodiment, as an alternative manner of the present invention, the set memory space may also be allocated for different learners. The independent memory spaces are allocated for different learners to run the behavior control codes of the respective target game roles, so that the problem that the memory space is overloaded due to the fact that a CPU is continuously occupied when the behavior control codes submitted by a certain learner run is avoided, the mutual isolation of the CPU and the memory is realized, and the influence of the behavior control codes of different learners is avoided when the behavior control codes of different learners run; that is, in order to ensure the safety of the system, in order to avoid the problem that the system is unstable due to the problems of the memory and the CPU in the learning equation sequence, the memory used by the program of the learner and the CPU are isolated, for example, one learning program uses a large amount of memory, if the memory used by the program of other learning parties is not isolated, each program uses own memory and CPU after the isolation, and the mutual influence is avoided. Meanwhile, the behavior control codes generated by the learner can be transmitted to a remote server for operation, and the learner does not need to download a client of a separate programming language operation environment.

It should be noted that, in general, a complex programming environment configuration link is required for a local learning programming mode, in this embodiment, the requirement on-line combat response timeliness is higher, and the user is complex and various, so that the operation mode of the remote server is selected, and only a multi-language programming environment is required to be configured at the server, so that multi-language operation effects such as PC, mobile phone, ipad and the like can be provided.

Example five

Fig. 5 is a schematic structural diagram of a programming learning device according to embodiment 5 of the present invention. The device can be realized by hardware and/or software, and the programming learning method provided by any embodiment of the invention can be executed, and has the corresponding functional modules and beneficial effects of the execution method. As shown in fig. 5, the apparatus includes:

a learning behavior data acquisition module 510, configured to acquire learning behavior data of recommended content by a learner in a programming mode; wherein the recommended content is determined from a programming knowledge graph.

The mastering situation determining module 520 is configured to determine a current mastering situation of the programming knowledge by the learner according to the learning behavior data.

According to the embodiment of the invention, the recommended content is determined from the programming knowledge graph, the learning behavior data of the learner on the recommended content in any programming mode is obtained, the current mastering condition of the learner on the programming knowledge in the recommended content is determined according to the obtained learning behavior data, the accurate and personalized content recommendation according to the behavior data generated in the learning process of the learner is realized, the teaching quality is improved, meanwhile, personalized content service aiming at the individual characteristics of the learner can be provided on a large scale, the learner can truly apply and verify the learned knowledge, and a higher learning target is achieved.

Optionally, the programming knowledge graph includes at least one programming knowledge category; the programming knowledge category includes at least one programming knowledge point; the knowledge points correspond to at least one content resource; the content resources include at least one of: knowledge points explain videos, test questions and practice checkpoints.

Optionally, the mastery condition determining module 520 is specifically configured to:

determining dimension scores of a learner in preset dimensions according to learning behavior data;

and determining the current mastering condition of the learner on the programming knowledge according to the dimension scores.

Optionally, the learning behavior data acquisition module 510 is specifically configured to:

determining the event level corresponding to the learner;

learning behavior data of a learner corresponding to an event level in an event mode is obtained.

Optionally, the mastery condition determining module 520 is further specifically configured to:

based on the preset code index, according to the learning behavior data, determining the current mastering condition of the learning party on the programming knowledge.

Optionally, the learning behavior data acquisition module 510 is further specifically configured to:

determining a target combat sub-mode in response to a selection operation of the learner on the sub-combat mode in the online combat mode;

determining a target game character in response to a selection operation of the target combat sub-mode sub-game character by the learner;

And acquiring behavior control codes written by the learner for the target game roles.

determining target execution actions of the target game roles according to the behavior control codes so as to control the target game roles to execute the target execution actions until the game is finished, and recording the game process;

and determining the current mastering condition of the learner on the programming knowledge according to the game process and the behavior control code.

Optionally, the apparatus further comprises:

and the memory space allocation module is used for allocating the set memory spaces for different learning parties.

Optionally, the learning behavior data includes at least one of: first learning behavior data of a knowledge point explanation video, second learning behavior data of an exercise checkpoint, third learning behavior data of a test question and program writing data; the first learning behavior data comprise a watching duration and/or watching times; the second learning behavior data includes at least one of: stay time, try times, whether to look over auxiliary prompt and whether to look back knowledge point explanation video; the third learning behavior data includes at least one of: the time length of the first answer, the accuracy of the first answer and the accuracy of the second answer.

The programming learning device provided by the embodiment of the invention can execute the programming learning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the programming learning method.

On the basis of the above embodiment, the present invention also provides a program learning system, including:

and the learner client is used for learning the recommended content by the learner in the programming mode and generating learning behavior data. Alternatively, the learner client may be an application in the mobile terminal.

Further, the programming learning platform can comprise a pre-class preparation module, an interlope type course module, an error question book module, a stage evaluation module, an expansion class module, a free creation module, a question adding module, an event module and an online fight module. The pre-class preparation module correspondingly provides relevant contents of the pre-class preparation mode; the relevant course module correspondingly provides relevant contents of the relevant course mode; the error question book module correspondingly provides related contents of an error question book mode; the stage evaluation module correspondingly provides related contents of a stage evaluation mode; the expansion classroom module correspondingly provides related contents of an expansion classroom mode; the free creation module correspondingly provides related content of a free creation mode; the exercise question adding module correspondingly provides relevant contents of an exercise question adding mode; the event module correspondingly provides related content of the event mode; the online fight module correspondingly provides the related content of the online fight mode.

Example six

Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a program learning method.

In some embodiments, the program learning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the program learning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the program learning method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a learner, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a learner; and a keyboard and pointing device (e.g., a mouse or a trackball) by which the learner may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a learner; for example, feedback provided to the learner may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the learner may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a learner computer having a graphical learner interface or a web browser through which a learner can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A program learning method, comprising:

2. The method of claim 1, wherein the programming knowledge graph comprises at least one programming knowledge category; the programming knowledge category includes at least one programming knowledge point; the knowledge points correspond to at least one content resource; the content resource includes at least one of: knowledge points explain videos, test questions and practice checkpoints.

3. The method of claim 1, wherein determining the current mastery of programming knowledge by the learner based on the learning behavior data comprises:

determining dimension scores of the learners in preset dimensions according to the learning behavior data;

and determining the current mastering condition of the learner on programming knowledge according to the dimension score.

4. The method of claim 1, wherein if the programming mode is an event mode; the learning behavior data of the learner on the recommended content in the programming mode is obtained, and the learning behavior data comprises:

determining the event level corresponding to the learner;

and acquiring learning behavior data of the learner at the corresponding event level in the event mode.

5. The method of claim 4, wherein determining the current mastery of programming knowledge by the learner based on the learning behavior data comprises:

based on preset code indexes, determining the current mastering condition of the learner on programming knowledge according to the learning behavior data.

6. The method of claim 1, wherein the programming mode is an online combat mode; the learning behavior data of the learner on the recommended content in the programming mode is obtained, and the learning behavior data comprises:

determining a target game role in response to a selection operation of the target combat sub-mode sub-game role by the learner;

7. The method of claim 6, wherein determining the current mastery of programming knowledge by the learner based on the learning behavior data comprises:

determining a target execution action of the target game role according to the behavior control code so as to control the target game role to execute the target execution action until the game is finished, and recording a game process;

and determining the current mastering condition of programming knowledge by the learner according to the game process and the behavior control code.

8. The method as recited in claim 1, further comprising:

and allocating the set memory space for different learners.

9. The method of any one of claims 1-8, wherein the learning behavior data comprises at least one of: first learning behavior data of a knowledge point explanation video, second learning behavior data of an exercise checkpoint, third learning behavior data of a test question and program writing data; the first learning behavior data comprise watching duration and/or watching times; the second learning behavior data includes at least one of: stay time, try times, whether to look over auxiliary prompt and whether to look back knowledge point explanation video; the third learning behavior data includes at least one of: the time length of the first answer, the accuracy of the first answer and the accuracy of the second answer.

10. A programming learning apparatus, comprising:

11. A programming learning system, comprising:

12. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the program learning method of any one of claims 1-9.

13. A computer readable storage medium storing computer instructions for causing a processor to implement the program learning method of any one of claims 1-9 when executed.